Abstract: Climate change is likely to affect vegetation dynamics. The spatial-temporal interannual change in vegetation index were analyzed, and both seasonal variation and variation in growing season were assessed based on the satellite-derived normalized difference vegetation index (NDVI) and the daily climate data collected from 408 meterological stations all over northern China from 1982 to 2015. Further, the response of NDVI to climate change, particularly climatic extremes were studied, using GIMMS NDVI 3g V1.0, daily temperature and precipitation datasets. Across the whole of northern China, linear regressions indicated a increasing trend of growing season NDVI values at rate of 0.002 (10 a)^-1 from 1982 to 2015, as temperature increased and precipitation decreased. The extreme-point symmetric mode decomposition (ESMD) method showed that the increasing rates of NDVI gradually intensified until 1992, followed by a slightly declining until 2005, and then gradually increasing again. There were spatial differences in the NDVI changes and the area of vegetation improvement accounted for 62.8% of northern China. The regions with significantly increased NDVI were mainly distributed in the Tianshan Mountain and northern Tarim Basin in northern Xinjiang, Qilian Mountains and mountain area of Longnan in Gansu, the hilly region of western Liaoning, and the Loess Plateau, Hetao Plain, Lüliang Mountain, and Taihang Mountains in Shanxi Province. Regions with significantly decreased NDVI were predominantly distributed in the Daxing'an, Xiaoxing'an, and Changbai Mountains. The greatest increase in NDVI in northern China was observed in cultivated vegetation, grassland, and desert vegetation. The growing season NDVI in northern China was positively correlated with temperature and precipitation, and was more responsive to temperature. There was an obvious spatial heterogeneity in the distribution pattern. The extreme climate index was used to assess the response of NDVI to climate extremes. All extreme temperature indices showed highly significant increasing trend, whereas only maximum one-day precipitation (Rx1day) and very wet days (R95p) showed significant increase in extreme precipitation. Temperature extremes had stronger impact on NDVI than precipitation extremes, with minimum temperature (TMINmean) and warm nights (TN90p) most closely related to NDVI. The results suggest that climate change and climatic extremes potentially have deeper and more complex effects on ecosystems than previously understood.

Key words: northern China, GIMMS NDVI 3g V1.0 dataset, temporal and spatial variation, climatic factor, climate extreme, response, extreme-point symmetric mode decomposition (ESMD)